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Water on an urban planet: Urbanization and the reach of urban water infrastructure

2014· article· en· W2155304622 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Environmental Change · 2014
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsMcGill University
FundersGordon and Betty Moore Foundation
KeywordsUrbanizationPlanetEnvironmental planningUrban infrastructureEnvironmental scienceWater resource managementUrban planningGeographyBusinessCivil engineeringEconomic growthEngineeringEconomics

Abstract

fetched live from OpenAlex

Urban growth is increasing the demand for freshwater resources, yet surprisingly the water sources of the world's large cities have never been globally assessed, hampering efforts to assess the distribution and causes of urban water stress. We conducted the first global survey of the large cities’ water sources, and show that previous global hydrologic models that ignored urban water infrastructure significantly overestimated urban water stress. Large cities obtain 78 ± 3% of their water from surface sources, some of which are far away: cumulatively, large cities moved 504 billion liters a day (184 km3 yr−1) a distance of 27,000 ± 3800 km, and the upstream contributing area of urban water sources is 41% of the global land surface. Despite this infrastructure, one in four cities, containing $4.8 ± 0.7 trillion in economic activity, remain water stressed due to geographical and financial limitations. The strategic management of these cities’ water sources is therefore important for the future of the global economy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.147
Teacher spread0.144 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it